Mineral analysis systems, such as the QEMSCAN and MLA available from FEI Company, Hillsboro, Oreg., have been used for many years to analyze mineral samples. To determine the type and relative quantity of minerals present in a mine, a sample in the form of small granules, is fixed in epoxy and placed in a vacuum chamber. An electron beam is directed toward a sample and, in a process called “energy dispersive x-ray spectroscopy” or “EDS,” the energies of x-rays coming from the sample in response to the electron beam are measured and plotted in a histogram to form a spectrum. The measured spectrum can be compared to the known spectra of various elements to determine which elements and minerals are present, and in what proportion. FIG. 1 shows a typical sample 100 having granules 102 embedded in epoxy.
It takes considerable time to accumulate an x-ray spectrum. When an electron in the primary beam impacts the sample, the electron loses energy by a variety of mechanisms. One energy loss mechanism includes transferring the electron energy to an inner shell electron, which can be ejected from the atom as a result. An outer shell electron will then fall into the inner shell, and a characteristic x-ray may be emitted. The energy of the characteristic x-ray is determined by the difference in energies between the inner shell and the outer shell. Because the energies of the shells are characteristic of the element, the energy of the x-ray is also characteristic of the material from which it is emitted. When the number of x-rays at different energies is plotted on a graph, one obtains a characteristic spectrum, such as the spectrum of pyrite shown in FIG. 2. The peaks are named for the corresponding original and final shell of the electron that originated the x-ray. FIG. 2 shows the sulfur Kα peak, the iron Kα peak and the iron Kβ peaks.
Other emissions besides characteristic x-rays are detectable when an electron beam impacts a sample surface. Emitted background or bremsstrahlung radiation x-rays are spread over a wide range of frequencies and can obscure characteristic x-ray peaks. Secondary electrons, Auger electrons, elastically and inelastically forward or back scattered electrons, and light can be emitted from the surface upon impact of a primary electron beam and can be used to form an image of the surface or to determine other properties of the surface. Backscattered electrons are typically detected by a solid state detector in which each backscattered electron is amplified as it creates many electron-hole pairs in a semiconductor detector. The backscattered electron detector signal is used to form an image as the beam is scanned, with the brightness of each image point determined by the number of backscattered electrons detected at the corresponding point on the sample as the primary beam moves across the sample.
Backscattering of electrons depends on the atomic number of the elements in the surface and upon the geometric relationship between the surface, the primary beam, and the detector. Obtaining a backscattered electron image requires collecting only a sufficient number of electrons at each point to produce a reasonable contrast between points having different properties and therefore is significantly quicker than obtaining a sufficient number of x-rays to compile a complete spectrum at each point. Also, the probability of an electron being backscattered is greater than the probability of the electron causing the emission of a characteristic x-ray of a particular frequency. Obtaining a backscattered electron image typically takes less time than acquiring sufficient x-rays to obtain an analyzable spectrum at a single dwell point.
In one mode of operating the MLA system, an image is first acquired using a backscattered electron detector, and the image is then processed to identify regions that appear from the contrast to have the same elemental composition. The beam is then positioned at the centroid of each identified region for a longer dwell time to collect an x-ray spectrum representative of the region.
When performing automated mineralogy on difficult samples using x-ray and back-scattered electron (BSE) information, BSE accuracy and repeatability are critical to differentiating minerals that have similar chemical formulas. For example, when analyzing iron ore it is important to accurately detect and differentiate between hematite (Fe2O3) and magnetite (Fe3O4). Although magnetite and hematite can be easily distinguished qualitatively using optical microscopy, quantitative characterization by automated scanning electron microscopy/energy dispersive x-ray spectroscopy (SEM-EDS), such as MLA, is challenging because hematite and magnetite are similar in their chemical composition and BSE brightness. The x-ray spectra for these minerals are nearly identical when collected on standard silicon drift detectors (SDD) with energy ranges of 20 eV at low x-ray counts. FIG. 3 shows an exemplary EDS x-ray spectrum 300 of magnetite (Fe3O4). FIG. 4 shows an exemplary EDS x-ray spectrum 400 of hematite (Fe2O3). A comparison of magnetite spectrum 300 and hematite spectrum 400 shows that the two spectra are very similar. Iron characteristic 102 and iron characteristic 202 are nearly identical. Oxygen characteristic 104 and oxygen characteristic 204 are also nearly identical. This similarity makes differentiating hematite and magnetite difficult for automated mineralogy applications. What is needed in the art is an improved method for automatically differentiating between minerals that have similar chemical formulas.